23,887 research outputs found
Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
We propose a new computationally efficient privacy-preserving identification
framework based on layered sparse coding. The key idea of the proposed
framework is a sparsifying transform learning with ambiguization, which
consists of a trained linear map, a component-wise nonlinearity and a privacy
amplification. We introduce a practical identification framework, which
consists of two phases: public and private identification. The public untrusted
server provides the fast search service based on the sparse privacy protected
codebook stored at its side. The private trusted server or the local client
application performs the refined accurate similarity search using the results
of the public search and the layered sparse codebooks stored at its side. The
private search is performed in the decoded domain and also the accuracy of
private search is chosen based on the authorization level of the client. The
efficiency of the proposed method is in computational complexity of encoding,
decoding, "encryption" (ambiguization) and "decryption" (purification) as well
as storage complexity of the codebooks.Comment: EUSIPCO 201
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image
scales to capture complementary coherent characteristics. We propose a method
for learning a hierarchy of synthesis dictionaries with an image classification
goal. The dictionaries and classification parameters are trained by a
classification objective, and the sparse features are extracted by reducing a
reconstruction loss in each layer. The reconstruction objectives in some sense
regularize the classification problem and inject source signal information in
the extracted features. The performance of the proposed hierarchical method
increases by adding more layers, which consequently makes this model easier to
tune and adapt. The proposed algorithm furthermore, shows remarkably lower
fooling rate in presence of adversarial perturbation. The validation of the
proposed approach is based on its classification performance using four
benchmark datasets and is compared to a CNN of similar size
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